1.1 Sample Information

A brief sample information is generated from the submission table for the following analysis.

1.2 Sample Section Information

Junior Epilepsy Sample Collection

Adult Epilepsy Sample Collection

1.3 Sample Slice Pictures

NS01 Slice Experiment Note

NS02 Slice Experiment Note

2.1 MERSCOPE Quality Summary

The summaries present the data quality assessment automatically generated by MERSCOPE for each experiment. We mainly focus on the transcripts level for each sample. So we’re looking for high density in transcripts, based on the transcripts count per field of view (FOV), transcript density in FOV, and frequency of transcripts detected.

Generally, log10 transcript count > 4.0 in most area can be considered as a good quality standard.

Need to note that the low accuracy in DAPI cell boundary is not a concern, as a self-designed cell segmentation processing will take over this task.

2.1.1 (Bad Quality!) 1300_A7

2.1.2 1300_B11

2.1.3 1463_TLE

2.1.4 1463_TLE_dup

2.1.5 NS02_FLE

2.1.6 NS01_TLE

2.2 Bad Sample: Low Cell Count Captured

Cell Detection Comapre on Bad Sample

3.1 Cell Segmentation & Filtering

Based on the spatial information and images obtained from MERFISH, we developed a machine learning model using the Cellpose algorithm to distinguish individual cells via MERFISH DAPI images.

To ensure the data quality and accuracy of cells, we have defined the minimum and maximum values for cell volume and gene count per cell. The cell volume should be between [100, 2500], and the gene count per cell > 25. After filter the outliers, the qualified cells count is shown in the following table.

Outliers were filtered from the data, and the qualified cell count is presented below. The transcript count Violin and transcript count Spatial Map are displayed here as part of the quality control reveal.

3.1.1 Cell Count after Filtering

3.1.2 Transcript Count Violin

Transcript Count Violin After Filtering

3.1.3 Transcript Count Spatial Map

Transcript Count Spatial Map After Filtering

3.2 Batch Effect & Dimension Reduction

We use Scanpy for the analysis of single-cell level transcriptome data. The initial stage of our analysis involves the elimination of batch effects, thereby ensuring that different samples from various batches are distributed within the same domain and are statistically reasonable to be integrated and compared. To achieve this, we utilize the Harmony algorithm.

Subsequently, we present visualizations of the batch difference by Leiden UMAP clusters. Also, we illustrate the distributions of the Leiden clusters for future analysis.

Umap of all samples combined cells

Umap of cells and colored by batch

4.1 Cell Type Umap

Cells are annotated from low resolution to high resolution as follows:

4.2 Cell Type Spatial Map

4.3 Glutamatergic-only Spatial Map

4.3 Non-Neuron Spatial Map

4.4 Cell Type Count Table

5.1 Reference

From literal search, we located a list of epilepsy-related gene. Within this recent published paper: Integrative analysis of epilepsy-associated genes reveals expression-phenotype correlations, they cross-referenced multiple public dataset (including publich database OMIM and two recent studies by Macnee et al and Oliver et al), and then category the epilepsy-related genes into three different groups:

CEG: core-epilepsy gene, where seizures are the dominant phenotype

DEEG: developmental and epileptic encephalopathy gene, that are associated with developmental and epileptic encephalopathy.

SRG: seizure-related gene, which are characterized by the presence of seizures and gross brain malformations.

5.2 Epilepsy-related gene expression in MERFISH data

From this dot plot, we can tell, most of the epilepsy-related gene are expressed in both excitatory and inhibitory neurons.

Some of the genes are associated to the identity of cell type markers, like GAD1, a notable excitatory marker. However, these genes may hold the potential to reveal variations in epilepsy disease level or serve as potential biomarkers for the condition.

6.1 all

6.2 Astrocyte

6.3 Endothelial

6.4 L2_3_4 IT

6.5 L5_6 IT

6.6 L5_6 NP

6.7 L6b

6.8 Lamp5

6.9 Microglia